Mechanisms of rainfall variability in the African
Sahel received little attention until the severe drought years of 1972
and 1973. The magnitude of the drought in those years was such that
it caused widespread crop failure and the deaths of thousands of
people. Since that time, a variety of studies, both in observational
and numerical modeling methods, [see reviews by Hastenrath (1988,
1990b)], have been employed for analysis and predictions of Sahel
rainfall.

In contrast to this, seasonal variability of Atlantic basin tropical
cyclones has intrigued researchers for almost a century. Garriott
(1906) attempted to relate the exceptionally high hurricane activity
in September 1906 to fluctuations in Atlantic basin surface pressure.
Subsequently, many researchers have attempted to relate the
frequencies and intensities of tropical cyclones to sea surface
temperature anomalies (Ray, 1935), general circulation patterns
(Namias, 1955; Ballenzweig, 1958), as well as surface pressure
variations (Shapiro, 1982a,b).

In the early 1980s, Gray (1984a, 1984b) showed strong predictive
relationships of seasonal tropical cyclone activity to the presence or
absence of an El Nino ño event, the phase and trend of the
stratospheric Quasi-Biennial Oscillation (QBO), and sea level pressure
anomalies in the Caribbean basin. Gray has since modified the
forecast scheme (Gray, 1990a, 1990b) to include 200 mb Caribbean zonal
wind anomalies and discounted temporal variability of the QBO phase.
Yearly forecasts and verifications exhibiting some skill in seasonal
predictions (Gray, 1990d) have been issued since 1984.

However, the fact that there is a strong interrelationship between
seasonal intense hurricane activity in the Atlantic basin and the
amount of rainfall which fell in the Sahel had not been fully realized
until very recently. This is somewhat understandable because named
storm activity (which receives more attention in the literature)
has not seen the types of decadal changes that the more
infrequent intense hurricanes have. Yet it is quite plausible that
such a relationship might be present since it is known that the same
weather systems which contribute especially large amounts of rainfall
to the Sahel also frequently serve as the genesis points for tropical
cyclones (Riehl, 1945). These, of course, are the African-spawned
easterly waves.

The first mention of a possible interannual connection between Sahel
rainfall and tropical cyclones variability appears in class notes from
Carlson and Lee (1978). They speculated that ``the severe drought
conditions" over the African Sahel might be related to ``the
diminution of hurricane activity'' in the Atlantic basin. They did not
pursue this further, however, nor did they appear to recognize the
primary association of intense hurricane activity and Sahel rainfall
conditions.

Gray (1987) revived interest in the possible Sahel rainfall and
hurricane association when he noted the similarity in his seasonal
measure of hurricane intensity and duration -- Hurricane Destruction
Potential -- and the strong downward trend in the Sahel rainfall,
which continued through much of the 1980's. However, it took a failed
seasonal forecast in 1989 (where Gray attributed his underestimation
to conditions associated with a return of rainfall to the Sahel) to
stress the importance of the intense hurricane--Sahel rainfall
relationship (Gray, 1989, 1990c) and to stimulate research on this
topic.

2. Definitions

Much confusion can arise when discussing the
various categories of tropical cyclones. The United States (U.S.)
Department of Commerce National Oceanic and Atmospheric
Administration (NOAA) (1977) technically defines tropical
cyclones as non-frontal low pressure synoptic--scale systems that
develop over tropical or subtropical waters and have a definite
organized circulation. Two additional requirements that should be
added: the cyclone must have a relatively warmer tropospheric inner
core structure than the environment (i.e., a ``warm--core") and the
maximum intensity of tangential winds should be located in the lower
troposphere.

The tropical cyclone designation is a broad term under which
various strength systems in the Atlantic basin are divided into:

Often, the term ``tropical storm'' refers to any cyclone of
tropical storm or hurricane strength. This type of reference
will be avoided. In this paper, tropical storms and hurricanes will
be collectively referred to as named storms [in deference to the
fact that since 1950 all tropical cyclones that were of at least
tropical storm force were given a name for identification (Neumann
et al., 1987), though some cyclones were determined to be of
tropical storm strength after the fact and thus lack a formal name].

The ``hurricane'' status can be further categorized by the severity of
the cyclone. The Saffir--Simpson (Simpson, 1974) Hurricane Scale
provides a measure of intensity from a Category 1 (Minimal) to
Category 5 (Catastrophic) hurricane. Table 1
summarizes the delineation of strength and effect. For purposes of this study it
became instructive to consider the Saffir--Simpson Category 3, 4, and
5 cyclones collectively as intense or major hurricanes .

One method for objectively determining the seasonal amount of tropical
cyclone activity is through the summed duration of each storm. This
partially removes the subjectivity involved in categorizing the
intensity of tropical cyclones. Dunn and Staff (1965) first utilized
the term hurricane days . A seasonal total of hurricane days is
the amount of days in which a hurricane existed (two existing
simultaneously count as two days). However, their calculations and a
similar one done by Gray (1984a) count a full hurricane day even if
the cyclone was of hurricane strength for just six hours of that 24
hour period. The computations for this report count days in one
quarter increments (six hour periods). This will slightly reduce the
numbers obtained by these earlier studies. Similar calculations have
been performed for intense hurricane days and named storm
days.

Another way to measure tropical cyclone variability on a seasonal
basis is through Gray's (1987) Hurricane Destruction Potential
or HDP . This measure gives a combined reflection of both the
intensity and duration of the tropical cyclones. It is defined as the
sum of the sustained wind speed (in knots) squared for every six hours
that the cyclone is of hurricane strength (65 kt or 33 m/s).
Hurricane Destruction Potential is defined to approximate the idea
that hurricanes can cause damage relative to the square of their wind
speed (i.e., a proxy of the summed kinetic energy of the cyclone's
maximum winds). Both the intense hurricane days and HDP are used
extensively in this report as measures of seasonal activity.

3. Data

a. Atlantic Basin Tropical Cyclones

The positions and intensities (sustained wind speed and
minimum surface pressure) of all Atlantic basin tropical cyclones of at
least tropical storm strength have been archived and are continually
being updated by the National Hurricane Center (NHC) in Miami, Florida.
(The `Atlantic basin' is defined as the tropical and subtropical regions
north of the equator in the Atlantic Ocean, the Caribbean Sea, and the
Gulf of Mexico.) This data set extends from 1886 to 1990 and is
described in detail by Jarvinen et al. (1984). This ``Best Track''
data set (as it is known since it is composed of the ``best'' estimate of
positions and intensities in a post-analysis of all data available) or
HURDAT (short for HURricane DATa) has been used quite extensively in our
Tropical Meteorology Project at Colorado State University.

We have followed the recommendations by Neumann et al. (1987) to
use tropical cyclone statistics based upon data since the mid-1940's,
when organized aircraft reconnaissance began, since this ``probably
best represents Atlantic tropical cyclone frequencies''. The same
logic follows for the day to day assessment of the intensity of
individual storms; again because in the earlier period ``storms that
were detected could have been mis-classified as to intensity''.

Another consideration in using tropical cyclone frequency and
intensity data is the subjectivity inherent in the categorization.
Satellite based tropical cyclone intensity estimations are pattern
recognition and empirical methods that do not directly measure the
storm's winds (Dvorak, 1977, 1984). Aircraft reconnaissance usually
records maximum wind speeds at flight levels (usually 850 or 700 mb).
These are instantaneous values and do not necessarily represent a very
accurate estimate of maximum surface winds. Nevertheless, satellite
and aircraft data usually give rather close independent intensity
estimates (Sheets, 1990). [Landsea (1991b) has determined that the
strength of the intense Atlantic hurricanes were slightly
overestimated 2--3 m/s during the mid 1940's to the
late 1960's. However, this bias is small and does not alter the main
conclusions of this study.]

Tropical cyclones that have affected the United States mainland have a
longer period of reliability concerning their frequency and intensity.
This is because of the large coastal populations that the U.S. has
along the Gulf of Mexico and Atlantic Ocean. It is unlikely that any
tropical storms or hurricanes were unnoticed crossing the coastline
since the late 1800's. Hebert and Taylor (1975) have made a
Saffir--Simpson categorization of all U.S. landfalling hurricanes from
1900 to 1974. They based their estimates primarily upon minimum
central pressures of the storm at time of landfall. Neumann et
al. (1987) have also analyzed 1899 and 1975 through 1986. This paper
has extended the analysis period through 1990 using NHC's end of the
season write-ups (Case and Gerrish, 1988; Lawrence and Gross, 1989;
Case and Mayfield, 1990; and Mayfield and Lawrence, 1991).

Besides the various categories of landfalling hurricanes, this report
also makes use of tropical storms that affected the U.S. This
information was also gleaned from the Neumann et al. (1987)
reference book and the recent annual reports.

Recently, Avila and Clark (1989) have revived the annual summary of
Atlantic basin tropical disturbances in the Monthly Weather
Review. These articles are companion papers to the annual summary of
Atlantic Basin tropical cyclones (see the original paper by Simpson
et al. (1968)). Since 1967, when daily satellite analysis made
it possible, NHC has attempted to report on the origins of tropical
cyclogenesis and the numbers and varieties of tropical disturbances.
L.A. Avila of NHC provided a detailed list of the origins of all
Atlantic basin tropical cyclones of at least tropical storm strength
from 1967 to 1990.

b. African Rainfall Data

The Sahel, lying between
approximately 11 and 20° in Africa, is the region which
separates the hyperarid Sahara Desert to the north and the rain forests
along the Gulf of Guinea and the Congo River basin to the south. It
is only during three to five months in the summer and early fall when
the Sahel receives substantial precipitation. This rainfall is due to
the annual cycle of the Intertropical Convergence Zone (ITCZ) which
reaches its northernmost position at that time. Over West Africa, the
ITCZ takes the form of low level southwesterlies (monsoonal flow)
which converge with low level northeasterlies. An excellent
qualitative overview of West African meteorology is given by Hayward
and Oguntoyinbo (1987).

For this study, ``African rainfall'' refers to any form of
precipitation. However, since temperatures in the Sahel range from 15
to 45C the only non-rain precipitation possible is hail,
that occurring only very infrequently.

The main statistical method used for analyzing rainfall variations is
the area-average normalization developed by Kraus (1977). To best
look at the regional aspect of rainfall variations, Kraus attempted to
combine stations without inducing a bias toward any station or any
subgrouping of stations. Using mean percentages for a group of
stations would favor the drier stations which experience huge
percentage variations of rainfall. Using mean absolute deviations
could slant the regional value toward stations with higher average
rainfall. To avoid either of these problems, Kraus used a
normalization of rainfall based upon the mean and standard deviation
at each station. This method is currently utilized by several
observational researchers in Sahel rainfall variability (Hastenrath,
1990a; Lamb et al ., 1990; Nicholson, 1986; and Shinoda, 1989) as
well as by the U.S. Climate Analysis Center (CAC) (U.S. Department of
Commerce, 1990) in their real-time analysis of U.S. and worldwide
precipitation patterns.

Using the following definition for the mean, r i , and for the
variance,
Sigma (i)2 at station i,

where rij is the period (e.g. weekly, monthly, multi-month) rainfall at station i
during year j and Ji is the number of years of data station i contains.The normalization for station i in year j is

which essentially tells how many standard deviations from the mean
that rainfall is for that particular year. The resulting regionally averaged
Index value of xij for the year j is defined as

where I j is the number of stations in the region available in
year j.

We have spent considerable effort in the gathering and analyzing of the African
rainfall data from a number of sources. Appendix A lists our African rainfall
data sources and the many individuals who have assisted us.

The beginning point in the rainfall analysis was chosen
to correspond to the first year Gray (1984a, 1984b) used in his studies---1949.
His analysis was limited to that date because of the lack of
tropical stratospheric data before then; this data being
essential due to the strong relationship between the stratosphere QBO and
Atlantic basin tropical cyclones (Gray, 1984a; Shapiro, 1989). This starting
date also agrees well with Neumann et al.'s (1987) assessment
(mentioned earlier) that quantitative climatological
studies on Atlantic basin tropical cyclones are most valid since the mid 1940's.

4. Intense Hurricane Variability

Landsea (1991b) has documented the intra-
and interseasonal climatology of intense Atlantic hurricanes
(Saffir--Simpson Category 3, 4, and 5). Three main features were
identified which differentiate these cyclones from weaker hurricanes
and tropical storms. First, intense hurricanes have experienced the
largest linear downward trend and show the most year to year
variation. The reduction in major hurricanes during the last twenty
years has also been observed in U.S. landfalling intense hurricanes,
especially along the U.S. East Coast and the Florida peninsula
(Sheets, 1990). Second, these storms account for three-quarters of
all U.S. tropical cyclone spawned damage even though they strike on
average only twice in three years. Third, since over 98% of the
intense hurricane activity occurs after July, this later starting date
of their annual cycle allows a prediction of occurrence to be made on
1 August -- two months after the beginning of the ``official''
hurricane season.

Figure 1 portrays the time series of intense
hurricane activity as measured both by seasonal number of
intense hurricanes and by seasonal number of days in which an
intense hurricane was present. Note the extreme reduction in
recent years as accentuated by the linear trend lines. The
42-year means for the intense hurricanes and the intense
hurricane days are 2.5 and 5.7, respectively. While all tropical
cyclone activity exhibit positive correlations with Sahel
rainfall, it is the intense hurricanes that show the strongest
association. This holds true even after these strong downward
trends are removed from the tropical cyclone data.

5. African Rainfall Variability

As described in the Introduction, Gray (1987) hypothesized
that the general downtrend in Atlantic basin tropical cyclone activity had a
connection to Sahel rainfall conditions. While the numbers of named storms and
hurricanes showed little multidecadal (1949--1969 and 1970--1986)
variation, the numbers and durations of stronger hurricanes (in
intense hurricanes, hurricane days, and HDP) varied by a factor of 2
to 1 between these periods. Gray noted that the analysis of Sahel
rainfall by Lamb (1985) also showed a large downward trend in the
data. More quantitatively, the twenty station Lamb Index, for the
years 1949 to 1990, is associated with intense hurricanes by a linear
correlation coefficient of r = 0.61 and with intense hurricane days at
r = 0.64. However, since Lamb's Index includes the western and
central Sahel and covers the multi-month period of April to October,
the signal that Sahel rainfall has with Atlantic basin tropical
cyclones may not be maximized. The following section isolates which
regions and time frames best relate the two phenomena.

a. Monthly Rainfall Variability

The most dominant feature in causing precipitation over the
continent of Africa is the ITCZ. Its latitudinal extent as measured by the 100
mm isohyet covers over 15° (1650 km) meridionally during
the Northern Hemisphere's summer. Direct extratropical influences are
relatively minor, only producing precipitation in the most poleward
> 30° latitude) extent of the continent. It is during the
Northern Hemisphere's monsoonal season when substantial rainfall
reaches north of 11°N that the Sahel gets almost all of its
precipitation (June to September). Precipitation during these four
months showed the strongest individual month associations with intense
hurricane activity.

Table 2 presents an analysis of the individual
months using linear correlation coefficients for individual station
monthly rainfall versus seasonal intense hurricane days
(Figure 2) . While each map shows areas
of both positive and negative correlation, these were
not accepted as showing meaningful significance unless they persisted
for at least three months at a correlation of |r|> 0.15 (a
subjective threshold of just 2 percent of variance explained) over a
region greater than 3° square ~100,000 km2. Only
one coupled region appears to meet the criteria: a strong positive
correlation throughout the Sahel with an associated weaker negative
correlation along the Gulf of Guinea. The Sahel consistently shows
this positive relationship from June to September, while the
negative dipole is seen for July through September. It is the
western and central Sahel which shows the highest correlations
versus intense hurricane days with several monthly correlation
values of at least r = 0.50. August has the strongest
association of any month with the seasonal hurricane activity.
The presence of an inverse relationship of Sahel rainfall with
the Gulf of Guinea region to its south is consistent with the
findings of Nicholson (1986). She showed that the Sahel and the Gulf
of Guinea often (but not always) show opposite anomalies during the
same year: when the Sahel has abundant rainfall, the area along the
Gulf of Guinea is usually dry; conversely, when the Gulf of Guinea
receives above normal amounts of rainfall, the Sahel is often drier
than normal.

Thus, the strongest monthly rainfall associations with
intense hurricane activity occur in the western and central Sahel
during the months of June to September with a maximum in August. The
following section presents the combined association of the four month
total rainfall.

b. June to September Rainfall Variability

Using combinations of the monthly correlations shown in the
previous section, it was determined that June to September Sahel
rainfall has the strongest concurrent relationship with intense
hurricane days. It is this four month period that comprises most
of the precipitation that occurs over the Sahel.

Confirming what was also observed in the Lamb Index, the Sahel shows
increasingly positive correlations with tropical cyclones as the
intensity category of the tropical cyclone increases. The three
analyses in Figure 3show
linear correlations for named storms,
hurricanes, and intense hurricanes versus individual station June to
September rainfall. The correlations were computed using data from
1949 to 1989, but as mentioned previously, many stations have very
spotty temporal coverage. Because of this, stations with even 12
years of data are included in the analysis (though several in the Sahel
have over 95 percent coverage).

Though all three analyses show a basic structure of positive
correlations throughout the Sahel (strongest at the westernmost
portion) and a weaker area of negative correlations along the Gulf of
Guinea, it is the magnitudes of the associations that are of note.
For the correlation with named storms, the highest values are only r =
0.35. For the strongest hurricanes, values of r = 0.65 and higher are
seen in the Western Sahel. Figure 4
is an enlargement of North Africa with the correlation coefficients for intense hurricane days versus rainfall.

6. Western Sahel Rainfall Index

a. Development of Index

An accepted way of creating rainfall indices for a particular region is to combine
stations through their mean standard deviations for that particular
period (as discussed in section 2). Thirty-eight stations are
utilized in such a method to create the June to September Western
Sahel Rainfall Index. Figure
5shows the locations of the stations
which comprise the index.

Countries included in the region are Senegal, Gambia, northern
Guinea-Bissau, southern Mauritania, and western Mali. All available
stations within the boundary are utilized if they provided at least 30
years of rainfall data in the period 1949 to 1990. Though this data
threshold may appear somewhat low (only 74 percent of years needed),
the quantity of data available over the Western Sahel is actually
more reliable than over much of the rest of tropical Africa.

Rainfall in the Sahel has shown a strong tendency for year-to-year
persistence during the last four decades. As
seen in Figure 6,
consistently wet anomalies were observed from 1950 to the mid-1960's
and then almost continuous dry anomalies were experienced from 1968
onward, with above average rainfall only in the years of 1969, 1975,
1988, and 1989. The tendency for decreasing precipitation amounts
with time is highlighted by the boldface trend line which gives the
least squares best fit the data. Removal of the linear trend in the
rainfall data is shown in a detrended analysis in
Figure 7. The strong
correlations between the Western Sahel rainfall and tropical cyclones
are not dependent upon concurrent trends in the data sets, however.

Table 2 provides the mean standard deviations of rainfall (the same
information as in Figure 6) as
well as the number of stations that were
included in each year's calculations. Data for the years of 1949 to
1950 and 1987 to 1990 are not as complete because the data sets from
which several stations are drawn were not available for those time
periods.

Information on the individual stations utilized in the Western Sahel
Index are provided in Table 3 . This region covers a wide meridional
area (11 to 20°N) and a large range of rainfall means. The
individual stations show a high degree of correlation with the entire
Western Sahel Index, with only 3 of 38 stations showing a correlation
of less than r = 0.62. This type of internal consistency is vital
where a single index is utilized in portraying precipitation for a
widespread region.

Table 4 shows a month by month analysis of the combined 38 station
means and standard deviations including the percent contribution to
the main June to September rainfall period and to the entire rainy
season of May to November. Though not shown, the remaining months,
December to April, receive in total, only about 1% of the annual
mean. Note that the main rainfall month is August, with both July and
September receiving considerable amounts as well. It is also seen
that with the higher means in July through September there is lower
overall variability (i.e., a lower coefficient of variation). The
higher variability during the fringe months of the monsoon (May to
June and October to November) is likely due to typically having no
rainfall or else receiving considerable amounts of rain (25--100 mm).
Nevertheless, Table 4 documents that the June to September period
chosen for inclusion in the Western Sahel Rainfall Index covers the
large majority of the annual rainfall of this region.

b. Association with Tropical Cyclones

The concurrent associations which the Western Sahel Index
has with various Atlantic basin tropical cyclone parameters are
presented in Table 5 , including a detrended analysis. The goal of
focusing on the relationship seen in the Lamb Index is achieved.
In agreement with Fig. 3 and 4, the association is strongest for the
most intense hurricane activity and weakest for the numbers of named
storms. Figure 8depicts the scatter plot
of the Western Sahel rainfall--intense hurricane days
relationship, where 58 percent of the variance between the two
are explained by a linear regression(Figure 9).
Note that except for named storms, all parameters are
significant beyond the 0.005 level using the one-tailed test
(Spiegel, 1988).

However, the methodology of selecting a posteriori the region with the
highest association induces artificial skill into the association
(Davis, 1976; Shapiro, 1984). It is likely that the amount of bias is
not large, as the Lamb Index (developed without regard to the
intense hurricane activity) also showed a linear correlation of r =
0.64 with intense hurricane activity. Therefore, a caveat is
recognized that significance testing for the relationships may
slightly overestimate the degree of association. Additionally,
independent data, though less reliable, verify the existence of the
association as discussed later.

One uncertainty regarding this relationship is whether the strong
correlations are simply due to coexisting trends in the data sets.
One test that can be done is to break the record into two periods and
then check the correlations. The obvious years that the break should
be used would be after 1969, as the long term drought began in 1970
and has proceeded relatively uninterrupted since. The correlations
with intense hurricane days then become:

1949 to 1969 (21 years - wet period).....r = 0.66

1970 to 1990 (21 years - dry period).....r = 0.66,

which are just moderately lower than the overall relationship of
r = 0.76 (a reduction of 14 percent of the variance explained). Since the
trend is essentially removed by stratifying the data into two separate
time periods, this confirms that the relationship is not trend dependent.

A second method for checking for trend induced associations would be
to remove any linear trend from both data sets. Table 5 showed the
detrended time series of the Western Sahel Rainfall Index. Though
both the rainfall index and the intense hurricane activity show very
substantial decreasing linear trends, removal of the trends has little
effect. Detrended intense hurricane days correlate with the detrended
Western Sahel Rainfall Index at r = 0.68 (Fig. 9), again moderately
lower than the r = 0.76 value presented earlier for the original
relationship. The remaining tropical cyclone parameters Table 5
show even less of a reduction in the detrended analysis.

For independent verification of the association, rainfall data from
Landsea et al. (1991) can be utilized in conjunction with
reliable data regarding
landfalling intense hurricanes along the US East Coast (see Fig. 10).
In the study, a five station Western Sahel rainfall index was created
for the years 1899 to 1990. For the years 1949 to 1990, the five
station index correlates at r = 0.91 (r = 0.85 in a detrended
analysis) versus the larger 38-station index shown here. Thus, the
five-station index likely represents the rainfall time series
adequately. For intense hurricanes striking the US East Coast, the
38-station index correlates at r = 0.45 (r = 0.29 in a detrended
analysis, significant beyond the 0.05 level) for the years 1949 to 1990. The
five-station index correlates independently at r = 0.20 (r = 0.19 in a detrended
analysis, significant beyond the 0.10 level) versus the US East Coast intense
hurricanes for the years 1899 to 1948. Thus, after accounting for uncertainties
due to less reliable rainfall and tropical cyclone data in the earlier period and
after removing the substantial trend mainly in the later period, a
confirmation of the Western Sahel--Atlantic tropical cyclone
association is seen in earlier independent data.

c.`Wet' Versus `Dry' Years

To facilitate discussions of `wet' and `dry' Western Sahel
rainfall years, the June to September Western Sahel Rainfall Index
values are ranked from wettest to driest for 1949 to 1990 in Table 6 . This will
allow composite analyses to be performed for groupings of wet versus dry
seasons.

Another use of this ordering by rainfall is to use rank correlations
versus tropical cyclone activity. The correlation coefficient
between ranked rainfall data and ranked intense hurricane days is r = 0.81, with
65% of the variance explained. This is slightly higher than the linear
correlation between the two (r = 0.76),
suggesting that a non-linear relationship as measured by the rank correlation
may be the best description of the association.

The reason for compositing parameters with respect to the wettest and
driest Western Sahel rainfall seasons is that physical differences between
the two regimes are accentuated. In a single season other factors (such as El
Niño, stratospheric QBO, etc) which show as much control on the tropical
cyclone variance as the Sahel rainfall may
obscure the tropical cyclone---African rainfall signal. This problem is
much reduced in a composite analysis. For the following analyses, the
ten wettest years (mean Western Sahel Index value of sigma = 0.95) are
contrasted with the ten driest years (mean value of sigma = --0.94).

Table 7 details how various tropical cyclone parameters differ in the
ten wettest and ten driest Western Sahel years. Significant
differences are seen in every category but are most pronounced for the
strongest hurricane activity: a five to one ratio in all intense
hurricane numbers and a ten to one ratio in the intense hurricane
days. Again, the statistical significance may be slightly exaggerated
because of the rainfall region selection methodology. While
the statistical significance for landfalling systems are not as high
as the entire Atlantic basin statistics, this is understandable due to
the small landfalling data set being tested. Day to day
characteristics of the mean steering flow are crucial to allowing
landfall along the U.S. coastline; thus, variability on a seasonal
basis for U.S. landfalling cyclones may contain more `noise'.

The U.S. landfalling intense hurricanes can be roughly separated into
two regions: the U.S. East Coast (including the Florida peninsula) and
the U.S. Gulf Coast (including the panhandle of Florida). Intense
hurricanes which strike the two regions have differing characteristics
such as the time of year which they come on shore and the origins of
the storms (Landsea, 1991b). Figure 10
illustrates the approximate separation point between the East
and Gulf Coasts for intense hurricane landfall. Note that while
the East Coast experiences extreme differences in wet versus dry
years in Table 7 , the Gulf Coast shows only
a moderate modification. Similarly to the U.S. East Coast, the
Caribbean Sea region also shows a strong modulation of hurricane
and intense hurricane numbers with respect to Western Sahel rainfall.
Since wet/dry differences in tropical cyclone frequency
(Fig. 11) are accentuated with a
component of duration (e.g. the named storm days, hurricane
days, intense hurricane days, and HDP in
Table 7 ), the cyclones are affected in their longevity in
addition to their intensity. This assertion is supported by
Table 8 , in which the durations of the
various tropical cyclones are contrasted by intensity. The most
striking results are seen in intense hurricane category where
wet regime intense hurricane lasted over twice as long on
average than dry regime intense hurricanes.
Figure 13 demonstrates the combination of more numerous and
longer lasting intense hurricanes in the wettest years versus
the fewer, shorter lived intense hurricanes in the driest years
by the paths these cyclones took. This figure also highlights
the differing number of strikes along the U.S. coastlines and
the Caribbean Sea.

One possible mechanism that can account for the variations of tropical
cyclones with Western Sahel rainfall is the differences which occur in
the genesis. Although just more than half of the tropical storms and
hurricanes in the Atlantic basin have been clearly demonstrated to
originate from easterly waves, over three-quarters of all intense hurricanes
have their origin from these waves (Landsea, 1991b).

A method to study the Western Sahel's rainfall effect on tropical
cyclone genesis is to stratify the origins of tropical cyclones by
Western Sahel Rainfall Index. However, as noted earlier, reliable
estimates of the genesis point of Atlantic basin cyclones has only
been available since 1967. Utilizing the ten wettest versus the ten
driest Sahel years would not be possible because the majority of the
wet years occurred before that date. An alternative would be to name
all years since 1967 from Table 2 with positive rainfall anomalies as
``wet'' years and those seasons with negative anomalies as ``dry''
years. This stratification splits the 24 years available into 5 wet
years (with a mean value of Sigma = 0.48) and 18 dry years
sigma= --0.68).

Table 9 shows the analysis of these wet and dry year composites. What
is uncovered is a relative 10 to 20 percent higher incidence of
easterly wave contribution to named storms, hurricanes, and intense
hurricane formation when the Western Sahel is wet. Also, wet years
show dramatic increases over dry years in the absolute numbers of easterly wave
spawned cyclones: 54% more named storms, 93% more hurricanes, and
136% more intense hurricanes. Thus, part of the increase in the
total numbers of cyclones spawned in wet years is due to more storms
forming from easterly waves. The non-African-wave spawned cyclones,
though not explicitly described in the table, show a slightly higher
incidence in the dry years. It is hypothesized that in the wet years
general circulation changes and local conditions over West Africa
favor more easterly waves to become tropical cyclones, especially
those developing into intense hurricanes. During the dry years, the
general circulation is unfavorable for easterly wave spawned storm
development, while the midlatitude spawned systems are slightly
enhanced.

7. Physical Mechanisms

Two mechanisms appear to account for
the consistent year-to-year covariation between Western Sahel rainfall and
intense Atlantic basin hurricane activity (Gray, 1990c). The first,
as illustrated in Figure
12, is that the general circulation is likely
altered to be unfavorable to tropical cyclogenesis and intensification
during Sahel drought years and more favorable during abundant rainfall
years in the Sahel. During the drought years, stronger upper
tropospheric westerly winds are developed which typically cause more
vertical wind shear -- a feature that has long been known (e.g. Gray,
1968) to be detrimental to tropical cyclones. As Kidson (1977)
pointed out, dry years in the Sahel are typically accompanied by a
weaker 200 mb tropical easterly jet over West Africa. Preliminary
evidence of the circulation differences in the Atlantic basin are seen
in Figure 13which shows a
comparison of Western Sahel rainfall and
Caribbean basin 200 mb zonal wind anomalies. Note that the higher
amounts of Western Sahel rainfall are associated with easterly zonal
wind anomalies.

Another plausible influence of Western Sahel monsoonal strength
variations on tropical cyclone activity is in the interannual modification
of easterly waves. These waves were shown by Dunn (1940), who termed
them westward traveling ``isallobaric waves'', to act as the
`seedling' circulations for tropical storms and hurricanes in the
Atlantic basin. It was shown in Table 9 that over 90% of all intense
hurricanes originate from easterly waves in wet Western Sahel years.

Since it has now been established that dry years in the Sahel
correspond to less intense hurricane seasons, it is possible that: 1)
the number of easterly waves per year is decreased (as suggested by
Druyan, 1989), 2) that the mean latitude where the waves travel is
altered, or 3) the amplitudes of the waves are diminished (also as
suggested by Druyan). The first hypothesis appears to not be valid.
Avila and Clark (1989) have shown that the number of waves originating
over Africa is very stable. Yearly averages of 58 waves with a
standard deviation of only 5 waves are observed. The second
hypothesis is possible but not likely to affect tropical cyclone
numbers and strengths unless the mean latitude is altered
substantially. Personal communication from satellite experts R. Zehr
and L. Avila do not suggest that this has happened.

Thus, the third hypothesis is the one needing most consideration.
Figure 14 gives an
idealized view that in wet Western Sahel years
(June to September) a larger number of waves emanating from Africa
have strong amplitudes (reflected in the streamlines at 700 mb and
surface pressure) and have more concentrated, persistent deep
convection. Some of these strong waves eventually develop into
intense Atlantic hurricanes. In dry years, substantially fewer waves
would be so organized and would show weaker or negligible convection
as well as having a weaker signature in the 700 mb flow field and
surface pressure.

The next obvious question then is do weaker easterly waves
cause the Sahel drought (as the waves with their embedded
squall lines are the major contributors to the monsoonal rainfall) or
are weaker easterly waves a result of the drought
conditions? It is also very possible that there is a feedback between
the two and that there is no clear cause and effect. This question
remains open at this time, but research is currently being conducted
to help answer it.

8. Summary

Seasonal Atlantic basin tropical cyclone activity, especially that of intense
hurricanes, is strongly related to concurrent Western Sahel rainfall.
The association is shown to be strong even after substantial linear
trends have been removed from the data sets. Additionally, an
independent analysis of earlier data confirms the existence of the
tropical cyclone--Western Sahel rainfall association.
Plausible physical mechanisms have also been introduced to explain the
association. An understanding of intense hurricane variations is
extremely important because of the huge potential destruction these
storms can cause and the recent emphasis on possible greenhouse gas
warming impacts upon their occurrence.

This new emphasis has taken the form of an American Meteorological
Society and the University Corporation for Atmospheric Research joint
statement (AMS Council and UCAR Board of Trustees, 1988) suggesting
that a potential greenhouse gas impact would be ``a higher frequency
and greater intensity of hurricanes''. Recent devastating cyclones
with Hurricane Gilbert in 1988, the strongest storm ever measured in
the Western Hemisphere (Willoughby et al., 1989), and Hurricane
Hugo in 1989, ``the most costly hurricane in the U.S. history'' (Case
and Mayfield, 1990), have only raised concerns higher.
However, with the results shown here, the modulation of intense
hurricane activity in the Atlantic basin is codependent upon rainfall
conditions in the Sahel. It is sadly ironic that when the Western
Sahel obtains a reprieve from its multidecadal drought with a return
of significant rainfall, it is very probable that the Atlantic basin,
especially along the U.S. East Coast and the Caribbean islands will
experience many more destructive intense hurricanes. One
should not identify a temporary return of intense hurricanes such as
occurred in 1988 and 1989 as being even partially influenced by
greenhouse gas warming. The association recognized here is probably
not anthropogenic, but is likely due to the natural variations of the
atmospheric-oceanic general circulation.

Acknowledgements The bulk of this paper was gleaned from research toward the first
author's Masters Thesis at Colorado State University with the second
author's tutelage (Landsea, 1991a). The first author had the privilege
of attending the WMO's Symposium on ``Meteorological Aspects of
Tropical Droughts with Emphasis on Long-Range Forecasting'' at Niamey,
Niger in the spring of 1990. While
there I had many excellent discussions of the Western Sahel rainfall
and tropical cyclone association with Yinka Adebayo, Birama Diarra,
Leonard Druyan, Graham Farmer, Stefan Hastenrath, Peter Hutchinson,
Peter Lamb, Kevin Lane, Robert Livezey, Tim Palmer, Randy Peppler,
Chet Ropelewski, M.V. Sivakumar, and Neal Ward. My time was well
spent as I gained a greater appreciation for the severity of Sahelian
drought conditions as well as for the people attempting to solve the
mysteries of long term droughts. It was truly a wonderful experience.

The authors are grateful for the African rainfall and Atlantic basin
tropical cyclone data as detailed in section 3 and Appendix A. Much
valuable computer programming expertise was contributed by Richard
Taft, William Thorson, and Todd Massey. Barbara Brumit, Laneigh
Walters, and Judy Sorbie-Dunn have provided important manuscript, data
analysis, and figure drafting assistance. Additional valuable
discussions were had with Professors Wayne Schubert, Roger Pielke,
Duane Stevens, David Randall, Paul Mielke, Kenneth Berry and Edward
Prill; with Research Associates John Sheaffer and Paul Ciesielski;
with fellow Gray project students Steve Hodanish, Ray Zehr, Dan
Mundell, Steve Hallin, John Knaff, and Mike Fitzpatrick here at CSU;
and with Lloyd Shapiro and Stan Goldenberg at the Hurricane Research
Division in Miami.

Appendix A

CSU AFRICAN RAINFALL DATA BASE

The primary data base for African rainfall is the ``World Monthly Surface
Station Climatology'' (WMSSC) managed by W.M.L. Spangler and R.L Jenne
at the National Center for Atmospheric Research (NCAR). Though WMSSC
provides surface data (precipitation, temperatures, surface pressures,
sea level pressures, and others) globally, our interest was primarily
precipitation (rainfall) data over Africa. The data collection,
extending from the mid-1800's to 1988, is available mainly from the
efforts of Prof. S.E. Nicholson of Florida State University for
additional historical data. Including stations located on nearby
islands (Azores, Canary, Madeira, Seychelles, and Madagascar), the
WMSSC data set has 584 African stations with rainfall information.
The Monthly Climatic Data for the World (MCDW) by the
National Climatic Data Center (U.S. Dept. of Commerce, 1989) has
provided an official monthly updating for the same stations through
December 1989. The data presented in MCDW is essentially what will be
used in the next updating of WMSSC.

A secondary data set that supplements the WMSSC
information was supplied to us by G. Farmer of the A.I.D. FEWS Project in
Arlington, VA. He provided
us with additional information on 36 WMSSC stations and data on 75
agricultural raingauges that have been available since 1951. These
stations are all in the Sahel region from Senegal in the west to Chad in
the east. In general, the quality on the 36 stations that overlapped
with the WMSSC stations was higher in the AID stations (e.g. less
rounding to the nearest 10 mm, less missing data, fewer unreasonable
outliers). Therefore, in combining the data sets together, we chose the
AID data as more reliable in the case where there was a 3 mm or greater
difference between WMSSC and AID for monthly rainfall amounts.

Additionally, E. O. Oladipo of Ahmadu Bello University provided us
with monthly rainfall data for stations in Nigeria. Recently, Nigeria
has been a difficult country to obtain data for. Professor Oladipo
has graciously provided us with data for 13 stations from 1970 to
1988. Since the data was coming directly from the stations involved,
this rainfall data was considered to be of high quality.

P.J. Lamb also provided us with the 20 stations used in his index of
West Sahel rainfall. He has used this data in connection with his
research in the underlying physical mechanisms for Sahel drought
(Lamb, 1982) as well as in a real--time monitoring of the Sahel in
conjunction with CAC (Lamb et al., 1990). The data was very
similar in quality to WMSSC yet provided information for some stations
which had missing data. Accordingly, we utilized Lamb's data to fill
data gaps but considered it lower priority than WMSSC, AID, and
Nigeria data in duplications.

The final addition that was used in this study was more recent data
provided us by D. Miskus and R.J. Tinker of CAC. They provided us
with CAC's best estimates of monthly rainfall as they are
able to infer from daily reports on the Global Telecommunications
System (GTS) going back to 1960. This information provided us
excellent preliminary data that will temporarily fill in gaps until we
receive updated reports from NCDC. Miskus and Tinker have also been
very generous in providing us data in real-time so as to enable us to
monitor the Sahel (as it now relates to Prof. W.M. Gray's (1990a,
1990b, 1990d) seasonal hurricane forecasts). Since these are
estimates (and can be somewhat erroneous due to missing and mistaken
reports) and not the official report, this data is only used in
absence of any other information (i.e., WMSSC, AID, Nigeria and Lamb).

Thus, the data sets used in this paper and the relative order of
priority can be summarized as:

Gray, W. M., 1987: Cyclic trends in hurricane destruction potential and
discussion of factors which will be used in the 1987 seasonal forecast.
9th National Hurricane Conference, Orlando, FL, 3 April 1987, 29
pp.

TABLE 2
Mean standard deviations and data availability (out of a
possible 38 stations per year) for the June to September
Western Sahel rainfall.

Year

Standard Deviation

Number of Stations

Year

Standard Deviation

Number of stations

1949

-0.10

24

1970

-0.45

37

1950

1.49

24

1971

-0.30

37

1951

0.32

37

1972

-1.16

38

1952

1.00

37

1973

-0.80

38

1953

0.55

38

1974

-0.23

38

1954

0.77

38

1975

0.34

38

1955

1.46

38

1976

-0.56

38

1956

0.40

38

1977

-0.91

37

1957

0.52

38

1978

-0.18

38

1958

1.28

37

1979

-0.54

38

1959

0.06

36

1980

-0.68

37

1960

0.51

35

1981

-0.34

37

1961

0.77

38

1982

-0.88

37

1962

0.19

37

1983

-1.31

37

1963

-0.09

38

1984

-1.16

37

1964

0.88

38

1985

-0.50

37

1965

0.54

37

1986

-0.37

37

1966

0.07

38

1987

-0.76

26

1967

0.75

37

1988

0.18

26

1968

-0.75

37

1989

0.58

25

1969

0.55

37

1990

-0.95

25

TABLE 3
The 38 stations used in the June to September Western Sahel
Rainfall Index: station name and country, June to September
mean and standard deviation (in mm), years of data in analysis,
correlation coefficients versus the Western Sahel Index itself,
and correlation coefficient versus intense hurricane days.

Rainfall data

Correlation coefficent

Station

Mean

SD

Years

Versus index

Versus intense hurricane days*

Nioro Du Sahel, Mali

483.9

159.75

42

0.809

0.511

Kayes, Mali

611.3

121.28

42

0.641

0.475

Kita, Mali

865.6

185.37

42

0.705

0.46

Segou, Mali

591.7

126.96

42

0.774

0.548

San, Mali

635.0

108.13

42

0.602

0.571

Kenieba, Mali

1045.2

241.41

40

0.768

0.50

Bamako/Senou, Mali

814.7

169.17

42

0.647

0.461

Koutiala, Mali

789.0

171.43

42

0.676

0.630

Bougouni, Mali

926.1

174.60

42

0.677

0.579

Sikasso, Mali

925.4

143.01

41

0.554

0.454

Atar, Mauritania

66.0

43.27

42

0.496

0.344

Akjoujt, Mauritania

58.7

45.29

40

0.628

0.555

Nouakchott, Mauritania

86.8

54.98

42

0.814

0.578

Boutilimit, Mauritania

133.3

76.44

42

0.734

0.615

Rosso, Mauritania

221.8

104.86

40

0.700

0.563

Kiffa, Mauritania

280.4

118.51

42

0.780

0.46

Saint Louis, Senegal

261.3

128.88

42

0.572

0.442

Podor, Senegal

232.3

120.71

42

0.706

0.620

Linguere, Senegal

399.7

115.33

42

0.747

0.632

Matam, Senegal

405.2

177.28

42

0.674

0.513

Dakar/Yoff, Senegal

421.2

177.83

42

0.853

0.680

Thies, Senegal

527.1

202.63

37

0.864

0.643

Diourbel, Senegal

544.5

177.29

42

0.864

0.632

Kaolack, Senegal

615.0

183.10

40

0.753

0.541

Tambacounda, Senegal

725.2

168.38

42

0.694

0.466

Bathurst/Yundum, Gambia

998.4

286.76

42

0.882

0.552

Bissau Airport, Guinea-Bissau

1542.1

337.73

40

0.773

0.574

Bansang, Gambia

823.7

290.44

34

0.781

0.562

Georgetown, Gambia

811.7

189.83

31

0.783

0.518

Basse Met, Gambia

842.1

221.00

34

0.774

0.698

Boghe, Mauritania

250.4

89.88

36

0.749

0.610

Selibaby, Mauritania

493.5

145.51

36

0.782

0.495

Louga, Senegal

336.3

157.19

31

0.825

0.570

Mbour, Senegal

576.8

216.54

36

0.834

0.538

Nioro Du Rip, Senegal

685.6

184.95

33

0.807

0.495

Velingara Casamance, Senegal

863.6

208.37

34

0.691

0.689

Sedhiou, Senegal

1094.6

303.71

36

0.764

0.553

Bambey Met, Senegal

538.5

151.72

36

0.871

0.586

*Significance level is 0.005 for all stations except 0.025 for Atar,Mauritania.

TABLE 5
Regression equations and correlation coefficients of June to
September Western Sahel Rainfall Index (x) versus Atlantic
basin tropical cyclone parameters (y) using data from 1949--1990
for both the original data sets and the detrended analysis.
(Asterisks refer to significance level: 0.100 for `*', 0.025 for `**'
and 0.005 for `***'.)

Tropical Cyclone Parameter

Correlation Coefficient

Regression equation

Standard error of y

Original data

Named Storms

0.35**

y = 9.42 + 1.406 x

± 2.8

Named Storm Days

0.56***

y = 47.15 + 15.052x

16.7

Hurricanes

0.47***

y = 5.85 + 1.404x

2.0

Hurricane Days

0.67***

y = 23.73 + 12.192x

10.1

Int. Hur.

0.71***

y = 2.47 + 1.805x

1.3

Int. Hur. Days

0.76***

y = 5.68 + 5.713x

3.6

HDP

0.73***

y = 73.21 + 47.936x

33.8

Detrended Data

Named Storms

0.34**

y = 0.05 + 1.862 x

± 2.8

Named Storm Days

0.54***

y = 0.12 + 19.053x

16.7

Hurricanes

0.41**

y = --0.02 + 1.575x

2.0

Hurricane Days

0.60***

y = 0.13 + 13.681x

10.1

Int. Hur.

0.59***

y = 0.03 + 1.766x

1.3

Int. Hur. Days

0.68***

y = 0.04 + 6.115x

3.6

HDP

0.65***

y = 0.52 + 51.916x

33.8

TABLE 6
June to September Western Sahel Rainfall Index ranked by
rainfall amounts in mean standard deviations from 1949 to
1990.

Rank

Year

Value Index

Rank

Year

Index Value

1.

1950

1.49

22.

1963

-0.09

2.

1955

1.46

23.

1949

-0.10

3.

1958

1.28

24.

1978

-0.18

4.

1952

1.00

25.

1974

-0.23

5.

1964

0.88

26.

1971

-0.30

6.

1954

0.77

27.

1981

-0.34

7.

1961

0.77

28.

1986

-0.37

8.

1967

0.75

29.

1970

-0.45

9.

1989

0.58

30.

1985

-0.50

10.

1969

0.55

31.

1979

-0.54

11.

1953

0.55

32.

1976

-0.56

12.

1965

0.54

33.

1980

-0.68

13.

1957

0.52

34.

1968

-0.75

14.

1960

0.51

35.

1987

-0.76

15.

1956

0.40

36.

1973

-0.80

16.

1975

0.34

37.

1982

-0.88

17.

1951

0.32

38.

1977

-0.91

18.

1962

0.19

39.

1990

-0.95

19.

1988

0.18

40.

1972

-1.16

20.

1966

0.07

41.

1984

-1.16

21.

1959

0.06

42.

1983

-1.31

TABLE 7
Summary statistics on the variability of tropical cyclone
parameters from the ten wettest Western Sahel years and the ten
driest years from 1949 to 1990. (Asterisks refer to
significance level: 0.100 for `*', 0.025 for `**' and 0.005 for
`***'.)

Tropical cyclone Parameter

Climatology (42 years)

Wettest Years' mean

Percent of normal

Driest years' mean

Percent normal

Ratio wet/dry

Named Storms

9.41

11.2

120

7.7

83

1.45***

Named Storm Days

47.2

67.0

143

34.0

73

1.97***

Hurricanes

5.85

8.0

138

4.6

79

1.74***

Hurricane Days

23.8

39.1

165

13.2

55

2.96***

Intense Hurricanes

2.48

4.5

180

0.9

36

5.00***

Intense Hurricane Days

5.69

12.4

214

1.2

21

10.33***

HDP

73.4

131.2

175

33.9

45

3.87***

U.S. Landfalling

Named Storms

3.30

3.4

103

1.9

58

1.79*

Hurricanes

1.82

2.1

130

0.7

43

3.00**

Intense Hurricanes

0.72

1.2

167

0.2

28

6.00***

I. H. Gulf Coast

0.37

0.5

147

0.2

59

2.50*

I. H. East Coast

0.35

0.7

241

0.0

0

infty**

Caribbean Sea

Hurricanes

1.18

2.1

178

0.4

34

5.25***

Intense Hurricanes

0.71

1.5

211

0.2

28

7.50***

TABLE 8
Variation in durations of named storms, hurricanes, and
intense hurricanes in the ten wettest and ten driest Western
Sahel years from 1949 to 1990. (Asterisks refer to significance
level: 0.100 for `*', 0.025 for `**' and 0.005 for `***'.

Tropical Cyclone Parameter

Climatology (42 Years)

Wettest Years' mean

Percent of normal

Driest years' mean

Percent of normal

Ratio wet/dry

Named Storms

4.9

6.0

122

4.4

90

1.36***

Hurricanes

3.9

4.9

126

2.9

74

1.69***

Intense Hurricanes

2.3

2.8

122

1.2

52

2.33***

TABLE 9
Mean annual easterly wave contributions of tropical cyclone
genesis for the Western Sahel Rainfall Index wet (5) and dry
years (19) as well as the entire 24 year dataset.

Wet years

Dry years

Twenty-four years

Tropical Cyclone intensity

All disturbances (#)

Waves (#)

Waves (%)

All disturbances (#)

Waves (#)

Waves (%)

All disturbances (#)

Waves (#)

Waves (%)

Named Storms

11.4

8.0

70

8.5

5.2

61

9.1

5.8

64

Hurricanes

7.2

5.8

81

5.0

3.0

60

5.4

3.6

67

Intense Hurricanes

2.8

2.6

93

1.4

1.1

80

1.7

1.5

87

LIST OF FIGURE CAPTIONS

1. Intense Atlantic hurricane activity from 1949 to 1990 by intense
hurricanes (upper panel) and intense hurricane days (lower panel). The solid
line is the least squares linear trend.

2. Smoothed values of linear correlation coefficient for individual
station rainfall versus seasonal intense hurricane days for June, July, August,
and September from 1949 to 1989. Contours are at r = ±0.15, 0.30 and 0.45.

3. Correlation coefficients of individual station June to September
rainfall versus named storms, hurricanes, and intense hurricanes from 1949 to
1990. Contours indicate values of r = ±0.15, 0.30, 0.45, and 0.60. Positive correlations are
within solid contours, while negative contours are indicated by dashed lines.

4. Same as Fig. 3 with June to September rainfall versus intense
hurricane days.

5. Location of rainfall stations which comprise the Western Sahel
Rainfall Index.

6. Mean standard deviations of rainfall for the 38 station June to
September Western Sahel Index. The boldface line indicates the least squares
best fit line to the data. Data presented are from 1949 to 1990.

7. Same as Fig. 6 with the downward linear trend removed from the
data.

8. Scatter plot of 1949 to 1990 values of June to
September Western Sahel Rainfall Index versus intense hurricane days.
The dashed line is the least squares best fit.

9. Same as Fig. 8 with linear trends removed from both data sets.

10. U.S. coastal regions that experience differing responses of
intense hurricane activity to Western Sahel rainfall. The approximate
separation point is the Apalachee Bay of Florida.

11. Intense hurricane tracks by 6 seven year groupings based upon
rankings of the June to September Western Sahel rainfall. Rain years 1--7
indicate the seven wettest years, while rain years 36--42 show the seven driest
years.

13. August to September Caribbean basin 200 mb Zonal Wind Anomalies
(ZWA) versus the June to September Western Sahel rainfall for the years 1949 to
1990. (Negative ZWA indicate easterly anomalies and positive indicate westerly
anomalies.)

14. Idealized portrayal of the easterly wave variations that are
suggested to occur during wet (upper panel) versus dry (lower panel) Western
Sahel years.